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R E S E A R C H A R T I C L E Open Access

Drift, selection, or migration? Processes affecting genetic differentiation and

variation along a latitudinal gradient in an amphibian

Maria Cortázar-Chinarro

1*

, Ella Z. Lattenkamp

1,2

, Yvonne Meyer-Lucht

1

, Emilien Luquet

1,3

, Anssi Laurila

1

and Jacob Höglund

1

Abstract

Background: Past events like fluctuations in population size and post-glacial colonization processes may influence the relative importance of genetic drift, migration and selection when determining the present day patterns of genetic variation. We disentangle how drift, selection and migration shape neutral and adaptive genetic variation in 12 moor frog populations along a 1700 km latitudinal gradient. We studied genetic differentiation and variation at a MHC exon II locus and a set of 18 microsatellites.

Results: Using outlier analyses, we identified the MHC II exon 2 (corresponding to the β-2 domain) locus and one microsatellite locus (RCO8640) to be subject to diversifying selection, while five microsatellite loci showed signals of stabilizing selection among populations. STRUCTURE and DAPC analyses on the neutral microsatellites assigned populations to a northern and a southern cluster, reflecting two different post-glacial colonization routes found in previous studies. Genetic variation overall was lower in the northern cluster. The signature of selection on MHC exon II was weaker in the northern cluster, possibly as a consequence of smaller and more fragmented populations.

Conclusion: Our results show that historical demographic processes combined with selection and drift have led to a complex pattern of differentiation along the gradient where some loci are more divergent among populations than predicted from drift expectations due to diversifying selection, while other loci are more uniform among populations due to stabilizing selection. Importantly, both overall and MHC genetic variation are lower at northern latitudes. Due to lower evolutionary potential, the low genetic variation in northern populations may increase the risk of extinction when confronted with emerging pathogens and climate change.

Keywords: Genetic drift, Natural selection, Major histocompatibility complex, Microsatellites, Outlier tests, Rana arvalis

Background

The relative roles of selection and drift shaping genetic diversity among and within natural populations are a contentious issue in evolutionary biology [1, 2]. Gener- ally, the force of genetic drift is inversely related to population size, while the opposite is true for selection [3]. At the molecular level, the most common form of

selection is purifying selection (either favouring or disfa- vouring allelic variants) which leads to loss of genetic variation. However, genetic loci can also be under vari- ous forms of balancing selection, which uphold and maintain variation over space and time [4]. As the effect of genetic drift is imminently linked with population size, variation in population size within a species’ range is expected to play a central role for the importance of genetic drift. As species are generally expected to achieve their highest abundances at the center of their range, and populations tend to become progressively

* Correspondence: maria.cortazar@ebc.uu.se

1

Animal Ecology/Department of Ecology and Genetics, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden

Full list of author information is available at the end of the article

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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smaller towards the edge of the range (the ‘abundant centre’ model; [5, 6]), drift is considered as more import- ant in populations closer to the range edge, leading into reduced genetic variation and stronger population struc- ture in these peripheral populations [7, 8].

While the abundant center model has been influential for much of the theory, evidence for populations being more numerous or frequent at the centre of the distribu- tion is, at best, limited [5]. Here, historical events like colonization processes can have a significant influence on patterns of genetic variation [8]. When new populations are repeatedly founded by a few individuals these founder effects will lead to loss of genetic variation [9–11]. In Northern Europe, the climate was considerably colder in the northern hemisphere during the Pleistocene, and most of the species currently present had withdrawn to refugia in Southern Europe [12, 13]. After the retreat of the gla- ciers, species expanded and recolonized the formerly uninhabitable areas [14, 15]. During the Holocene recolonization genetic variation was lost due to repeated founder events. As a result, less diverse populations are often found at northern latitudes [16–18], and these popu- lations are predicted to genetically differ from the south- ern ones due to their demographic history.

Disentangling whether current patterns of genetic vari- ation along geographical gradients are brought about by central-marginal or colonization processes is not simple, because, due to the location of glacial refugia, the two patterns are geographically correlated [8]. Moreover, these processes can be complicated by historical pro- cesses when populations following different migration routes meet in a secondary contact zone [8] and con- founded by the fact that divergent selection in different parts of the species range may also cause population di- vergence [14]. However, such information is pivotal for understanding the genetic processes in contemporary populations.

In population and conservation genetics, spatial popula- tion structure and gene flow are usually assessed with neutral markers, assuming that this variation also reflects adaptive potential [19] However, the use of only neutral markers has important limitations and does not provide a complete picture on genetic variation and the evolutionary potential of wildlife populations [20 –23]. In contrast to neutral markers, loci of the Major Histocompatability Complex (MHC) comprise well understood coding genes which show a high level of variation [24 –28]. The MHC is a multigene family and the most polymorphic functional set of loci in vertebrate genomes described so far [29 –33].

It codes for proteins that are part of the adaptive immune system and associated with disease and parasite resistance [4, 34 –36]. Previous studies have shown a direct associ- ation between specific MHC alleles and pathogen resist- ance (e.g. [24, 37 –40]). However, other studies have

claimed a lack of association between disease resistance and MHC genetic variation [41, 42].

Amphibians are globally subjected to severe declines and currently the most threatened vertebrate class [43].

Along with the severe impact of habitat fragmentation, emerging infectious diseases are accounting for the glo- bal decline such as the chytridiomycosis caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), which has caused a large number of local and global am- phibian extinctions [44, 45]. Several studies have empha- sized the importance of MHC variation for disease resistance in amphibians [46]. For example, transcrip- tomic studies show that Bd infection activates immune pathways in many species [47, 48], and MHC heterozy- gous individuals or individuals with unique MHC alleles have been shown to be resistant to Bd [38, 39, 49] and other pathogens [50, 51]. The study of MHC variation is especially important in amphibian populations at north- ern latitudes, where genetic variation is often lower due to postglacial colonization processes. Indeed earlier stud- ies in two amphibian species (Triturus cristatus: [52];

Epidalea calamita: [53, 54]) have found low MHC vari- ation in high-latitude populations, potentially rendering these populations especially vulnerable to disease. With these exceptions, little is known about how adaptive genetic variation in general, and MHC variation in par- ticular, varies with latitude in amphibians.

Studies on life-history variation of ranid frogs in northern Europe have revealed extensive adaptive gen- etic divergence in larval development rates, both at local scale (Rana temporaria; Rana arvalis; [55 –57] and over wider latitudinal gradients (Rana temporaria; Pelophylax lessonae [58, 59]. Here the thyroid hormone pathway has an important dual function repressing thyroid- induced gene expression in premetamorphic tadpoles and, on the other hand, activating thyroid-induced genes to initiate metamorphosis (Xenopus laevis; [60, 61]). The microsatellite locus RCO8640 located inside the up- regulated transcription factor gene C/EBP is involved in the thyroid hormone pathway (Lithobates catesbiana;

[62, 63], and has been linked with adaptive divergence in larval developmental rate (Rana arvalis; [56, 57]) provid- ing an additional marker of adaptive genetic variation.

In this study, we assessed genetic variation in 12

moorfrog Rana arvalis populations along a 1700 km

latitudinal gradient from northern Germany to northern

Sweden. Sampling along this gradient allowed us to

study variation in the single MHC class II exon 2 locus

and 18 microsatellites in order to: 1) trace the postglacial

history of the moor frog along the gradient, 2) study the

level of population genetic diversity along the gradient

in order to elucidate adaptability to more local environ-

ments, and 3) investigate possible different effects of se-

lection along the gradient. Our ultimate goal is to

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understand how these processes combine to create a complex pattern of within and among site genetic diver- sity of R. arvalis along the latitudinal gradient.

Results

Microsatellites – Null alleles and detection of loci under selection

Out of 28 initially tested microsatellite loci, three (Rtempμ9, Rtempμ7, Rt2Ca9) were monomorphic, three (WRA1–22, WRA1–28, WRA6–8) failed to amplify and four (RtμJ, RNTYR2, RNTYR2, RtCa11) produced am- biguous scoring patterns and were therefore excluded from subsequent analyses (Additional file 1: Table S1).

The remaining 18 microsatellites were polymorphic in all populations with 6.3 alleles per locus on average.

Three out of the remaining 18 loci (EU_11, RECALQ and RtCa41) were found to contain null alleles and were removed from further analyses. Five loci (RRDD590, Rtca18, RtuP, RtCa25, and WRA_160) were identified to be under stabilizing selection with F

ST

lower than 0.03, nine loci were identified as neutral (R1atCa17, Rtempμ4, Rtempμ5, RCIDII, EU_06, EU_12, EU_15, EU_19, EU_24), whereas the locus RCO8640 was under diversi- fying selection with high F

ST

(0.48; Additional file 1:

Table S1 and Additional file 2: Table S2). All loci identi- fied as outliers by Lositan and/or BayeScan were deemed as “under selection” and excluded from the neutral ex- pectation analyses (Additional file 2: Table S2). All loci that were under diversifying selection, stabilizing selec- tion, identified either by Lositan or Bayescan, or neutral expectations when the MHC II exon 2 was included in the analyses are summarized in Additional file 3: Table S3. The MHC II exon 2 and RCO8640 were under diver- sifying selection in the full gradient and in the southern cluster but not in the northern cluster (Fig. 1, Additional file 3: Table S3).

MHC II exon 2 diversity: Miseq run summary

We obtained a total of 20 million of MiSeq sequence reads in two separate runs, an average of 2.5–2.7 million

reads per pool with intact primers and attached barcode information that could be assigned to a total of 361 amplicons from 207 individuals. In total 33 samples out of 240 failed due to PCR amplification problems. The average number of reads per amplicon was 13,404, ran- ging from 300 to 53,006; five amplicons with <300 reads were discarded. We amplified and sequenced 111 of the 207 genotyped individuals in two or three replicates, which corresponds to a replication rate of 53.6%, includ- ing every amplicon that revealed a unique MHC allele.

Replicates were randomly assigned across different pools to avoid false allele identification. All replicates revealed the identical genotype as the respective original sample, leading to a genotyping reproducibility of 100%. Among the 207 individuals, we assigned 57 valid MHC II exon 2 alleles with a length of 272 bp and 27 polymorphic nu- cleotide positions. None of these sequences showed in- sertions, deletions or stop codons, therefore we assume that these are true alleles. All the 57 valid MHC II exon 2 allele sequences were translated into unique amino acid alleles. We checked for signs of recombination by using Omegamap [64] and did not find any signal of re- combination in MHC class II exon 2. By following the DOC method [65], we detected a single MHC class II locus in 206 individuals. One sample (A10; Germany), however, revealed a second MHC class II locus in appar- ently lower read numbers in two of the three replicates, pointing to the possible existence of a very rare MHC class II duplication. We conclude that we are working with a single MHC class II locus in our data set.

Genetic structure among populations

The neutral microsatellites markers showed a global F

ST

of 0.19 (95% C.I. = 0.14–0.26). In the northern cluster (see below) F

ST

for neutral microsatellites was 0.12 (95%

C.I. = 0.05–0.16) and in the southern cluster 0.09 (95%

C.I. = 0.00–0.15). Population differentiation increased with geographic distance, showing clear and significant IBD (R

2

= 0.54; p = 0.003; Additional file 4: Figure S1).

Using the STRUCTURE admixture model, the most

-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0 0.2 0.4 0.6 0.8 1

Fst

He

RC08640

RRDD590 RtUPWRA_160

MHC

-0.05 0 0.05

0.1 0.15 0.2 0.25 0.3 0.35 0.4

0 0.2 0.4 0.6 0.8 1

Fst

He

-0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65

0 0.2 0.4 0.6 0.8 1

Fst

He MHC

RCO8640

RtUP

a) b) c)

Fig. 1 F

ST

vs expected heterozygosity for each 15 microsatellite and the MHC II exon 2 locus. Black dashed lines show the upper and lower 99%

confidence intervals with 10,000 simulations from a stepwise mutation model (SMM), loci under neutrality expectations are colored in grey, loci

under differential selection are colored in yellow and loci under diversifying selection are colored in red. Figure a represent the plot for the entire

gradient, figure b the southern cluster and figure c northern clusters, respectively

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likely number of genetic clusters was two (K = 2), reveal- ing a northern (Umeå and Luleå) and a southern cluster (Uppsala, Skåne and Germany). Further STRUCTURE runs suggested population substructure (Additional file 5: Figure S2). The Uppsala and Germany populations seemed to be more similar to each other than to the Skåne populations when K = 3 was modelled. Four dif- ferent clusters were observed when K = 4 was modelled while the same four clusters were observed with K = 5 since the northern populations (Luleå and Umeå) were not differentiated. A similar result was also found in the DAPC analyses which revealed two well defined clusters (a southern and a northern) with further substructure in concordance with the STRUCTURE results (Additional file 6: Figure S3). We did not find any evidence of gene flow between any of populations in the entire gradient using divMigrate [66]. This was the case even among local populations within the regions.

The global F

ST

for MHC II exon 2 in the entire gradient was 0.36 with the German populations included (95% C.I.

= 0.32–0.40) and 0.42 (95% C.I. = 0.23–0.47) when ex- cluded. We found significant IBD (R

2

= 0.53; p = 0.031;

Additional file 4: Figure S1). F

ST

was 0.14 (95% C.I. = 0.07–

0.18) and 0.28 with the German populations included (95%

C.I. = 0.21–0.33) within the northern and southern cluster, respectively and 0.35 in the south when German popula- tion were excluded (95% C.I. = 0.24–0.42). The populations were clearly differentiated and genetically structured (Add- itional file 7: Table S4). Allele frequency pie-charts showed a structured pattern from northern to southern popula- tions forming two clearly different clusters - a northern (Luleå and Umeå) and a southern (Uppsala, Skåne and Germany) cluster - for the MHC gene (Fig. 2; Additional

file 8: Figure S4). F

ST

between these two clusters was 0.42 (p < 0.001). Surprisingly, we found several alleles at very low frequencies at intermediate latitudes (Uppsala) and only five alleles were shared between Umeå in the north and the southern populations (Fig. 2; Additional file 8:

Figure S4 and Additional file 9: Table S5).

The outlier microsatellite locus RCO8640 showed strong differentiation along the gradient with an overall F

ST

of 0.45 (95% C.I. = 0.35–0.55). F

ST

was 0.33 (95%

C.I. = 0.20–0.47) and 0.32 (95% C.I. = 0.23–0.45) within the northern and southern cluster, respectively. Four dif- ferent alleles were found at this locus. In Luleå, the northernmost populations in the gradient, only one al- lele was present, and in Umeå this was the most com- mon allele (Fig. 2; Additional file 8: Figure S4).

When comparing differentiation in loci under diversi- fying selection (MHC and RCO8640) and neutral micro- satellites we found that the Restricted Major Axis (RMA) regression slope for MHC differentiation (F’

ST

) against neutral differentiation (G’

ST

) tended to be higher in the southern (slope = 1.89 (95% C.I.: 0.93–3.55) com- pared to the northern cluster (slope = 0.76 (95% C.I.:

0.35–1.59, Fig. 3) possibly indicating stronger differenti- ation on MHC in the south. The corresponding RMA slopes for RCO8640 were 2.19 (C.I.: 1.68–2.85) in the southern and 1.41 (C.I.: 0.52–4.00) in the northern clus- ter (Fig. 3), again suggesting a trend of stronger differen- tiation in the south although the confidence intervals of the RMA slopes were widely overlapping.

Genetic diversity within populations

For the nine neutral microsatellites, H

E

within popula- tions ranged from 0.08 to 0.32 (overall H

E

= 0.28) and

(B)

(E)

(A)

(M)

(Se)

(H)

(N)

(AÖ)

(V) (R)

(S)

(T)

a) b) c)

(B)

(E)

(A)

(M)

(Se)

(H)

(N)

(AÖ)

(R)

(S)

(T) (V)

Fig. 2 a Approximate distribution of R. arvalis in Europe. The map is based on information in Gasc et al. [116] (blue dots and open circles), and updated with information from the Swedish Species Information Centre (http://artfaktaartdatabankense/taxon/208250), Sillero et al. [84], and own observations (black dots). b Allelic distribution of MHC Class II alleles and c RCO8640 in 12 R arvalis populations (B: Besbyn (Luleå); F: Ernäs (Luleå);

N: Nydalasjön (Umeå); H: Holmsjön (Umeå); ÖA: Österbybruk (Uppsala); V: Valsbrunna (Uppsala); R: Räften (Skåne); S: Sjöhusen (Skåne); T: Tvedöra

(Skåne), M: Mardorf (Germany), Se: Seebeckwiesen (Germany). A: Altwarmbüchen (Germany)). Colour coding scheme for MHC alleles is given in

the (Additional file 5: Fig. S2)

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allelic richness from 1.45 to 3.0 (overall AR = 2.22). Ob- served and expected heterozygosity were not signifi- cantly different, as would be expected of selectively neutral loci. We found higher H

E

and AR values in the southern (i.e., Uppsala and southwards) than in the northern regions (Umeå and Luleå; Table 2). We did not find any significant relationships between latitude and H

E

or latitude and AR, along either part of the gradient (Fig. 4). The maximum number of alleles was found in the Uppsala region (46) followed by Germany (45).

Across all 207 individuals we found 57 alleles at the MHC class II β locus (Fig. 2; Additional file 8: Figure S4). The number of alleles per population varied sub- stantially among regions. Levels of expected heterozy- gosity within populations for the MHC locus ranged from 0.31 to 0.91 (overall H

E

= 0.71) and allelic richness ranged from 2.98 to 10.09 (mean AR = 6.45; Table 1).

Levels of observed heterozygosity within populations ranged from 0.30 to 0.90 (overall H

O

= 0.56; Table 1).

Observed and expected heterozygosity were significantly deviant in five out of 12 populations (HWE equilibrium tests; p < 0.001). Especially, the German populations (A, M and Se) showed lower H

O

values than expected (H

O

= 0.35, H

E

= 0.85; H

O

= 0.30, H

E

= 0.87 and H

O

= 0.50, H

E

= 0.91, respectively) compared to the rest of populations (Table 1). These low values of observed heterozygosity might be the result of an amplification problem of some alleles in these populations, leading to an artificial increase in homozygotes. The northern re- gions (Umeå and Luleå) showed lower diversity than the southern (all remaining) populations in terms of H

E

and AR (Fig. 4; Wilcoxon test AR; p < 0.001 HE; p < 0.001).

There was no significant relationship between latitude and H

E

and AR in the southern part of the gradient.

However, along the northern part of the gradient H

E

was lower in the northernmost region (Luleå, Fig. 4 and Table 1). The highest number of alleles (46) was again found within the Uppsala region. Within the entire

Fig. 3 Standardized F ’

ST

pairwise comparisons a) for MHC class II and b) RCO860 microsatellite marker b) in relation to Standardized G ’

ST

pairwise values for neutral microsatellites. The northern cluster is represented by yellow circles and the southern cluster is represented by blue circles

MHC MHC

MS MS

a) b)

Fig. 4 MHC genetic variation (blue circles (southern cluster). and green circles (northern cluster). Microsatellite variation is given in pink tringles

(southern cluster) and orange triangles (northern cluster). The linear regression is represented by a black line. a) H

E

= Expected heterozygosity and

b) AR = allelic richness

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gradient, 37 out of 57 alleles were private to a single population (Table 1). The number of private alleles within a sampling area was highest in A (Germany, PA = 12) and ÖA (Uppsala, PA = 15) populations (Add- itional file 9: Table S5). Only one allele (Raar_DAB*15) was widespread and observed in 9 populations from dif- ferent regions, while two alleles (Raar_DAB*20 and Raar_DAB*21) were only shared between the 4 popula- tions in northern Sweden (Umeå and Luleå regions; Fig. 2;

Additional file 8: Figure S4; Additional file 9: Table S5).

Discussion

We studied the MHC and microsatellite genetic vari- ation in R. arvalis populations along a 1700 km latitu- dinal gradient and assessed the relative contributions of drift, selection and migration/colonization to understand the postglacial colonization history and the evolutionary forces acting on the adaptive potential and genetic vari- ation of the populations. Four main results can be de- rived from our analyses. First, the postglacial migration history has resulted in two major clusters currently present in northern Germany and the Scandinavian Peninsula: a northern and a southern. Second, within population genetic variation is higher in the southern as compared to the northern cluster for all the studied gen- etic markers. Third, there are indications that selection is likely weaker and drift stronger in the northern clus- ter. Fourth, these forces combined have led to a complex pattern of differentiation along the gradient where some loci are more divergent among populations than pre- dicted from drift expectations due to diversifying selec- tion, while other loci are more uniform among

populations due to stabilizing selection. We will discuss each of these conclusions in detail below.

Postglacial colonization history

Previous phylogeographical studies of R. arvalis suggest two routes of postglacial recolonization from south- eastern Europe to Scandinavia [67, 68]. Accordingly, R.

arvalis expanded from refugia in the Balkans and south- ern Russia, and used two different postglacial recolonization routes to colonize the Scandinavian Peninsula; one crossing from western central Europe to Sweden from the south-west via the postglacial land bridge that connected Denmark and Sweden, and the other using a route on the eastern side of the Baltic Sea via Finland to northern Sweden [67, 68]. Under this sce- nario the two lineages met in northern Sweden forming a contact zone, which exact location remains to be for- mally identified. Our results are in accordance with the previous studies. However, our study suggests that the contact zone lies further south (i.e. between Uppsala and Umeå) than the previous, primarily mt-DNA based, ana- lysis [68], which placed the contact zone between Umeå and Luleå.

Within region diversity

Our results show lower genetic variation for both neu- tral microsatellites and the MHC II exon 2 locus in pop- ulations from the north of Sweden as compared to southern populations. For the MHC II exon 2 locus this difference is especially pronounced in the northernmost sampling region (Luleå), where we only found six of the 57 alleles present along the gradient. These results are in concordance with studies on different taxa, which have Table 1 Genetic variation at the MHC II exon 2 locus in the populations

Locality Sampling area code n NA As PA H

o

H

e

AR

Altwarmbüchen A 14 8 3 0.35

*

0.85 7.53

Mardorf Germany M 10 8 17 3 0.30

*

0.87 8.00

Seebeckwiesen Se 10 9 2 0.50

*

0.91 9.00

Sjohusen R 20 14 2 0.80 0.68 4.90

Tvödöra Skåne S 20 6 18 7 0.90 0.86 10.09

Räften T 19 6 1 0.42

*

0.54 4.62

Österbybruk AÖ 18 11 5 0.83 0.87 8.49

Valsbrunna Uppsala V 19 11 19 7 0.78 0.79 8.07

Holmsjön H 19 5 0 0.57 0.64 4.01

Nydalasjön Umeå Ny 19 9 10 3 0.57

*

0.76 6.63

Besbyn B 20 4 2 0.30 0.31 2.98

Ernäs Luleå E 19 4 6 2 0.42 0.44 3.05

Total 207 37 0.56 0.71 6.45

The populations are ordered from South to North. n = number of individuals; NA = alleles within a population; As = alleles within a sampling area; PA = private alleles; Ho= observed heterozygosity, He= expected heterozygosity; AR = allelic richness. The HO that deviate significantly from H-W expectations are marked with a*

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found lower genetic variability in northern Europe as compared to central European populations [13, 16], and this is usually interpreted as a consequence of northern Sweden being the last area in Europe to be recolonized after the last glaciation events. This is also what is pre- dicted by the central marginal hypothesis of genetic vari- ation [7, 8]. However, in the case of R. arvalis, our analyses showed that the observed clinal variation is not simply due to a gradual decline along the colonization gradient as the two lineages colonizing the Scandinavian Peninsula meet at intermediate latitudes somewhere be- tween Uppsala and Umeå. This leads to a prediction that these two localities are the least diverse along each of the respective colonization routes. However, the north- ernmost locality, Luleå, is the one showing the lowest within site diversity.

The depletion in genetic variation observed in the north suggests lower adaptive potential in response to climate change in northern populations [69]. The re- duced variation in MHC exon II in the northern cluster is in line with the earlier results on two amphibian spe- cies [52–54]. Although these results suggest long-term survival of populations with very low MHC variation in postglacial expansion areas for hundreds of generations, the low number of MHC II alleles in the northern popu- lation (Luleå) can be a disadvantage in the future if the populations are confronted with novel or emerging diseases [70, 71].

The high levels of expected heterozygosity, the large number of rare alleles - numbers exceeding or being in line with what has previously been reported for refugial populations in unglaciated areas of Europe [53] – as well as the heterozygosity excess for the MHC exon II in some southern populations might be explained by two hypotheses on pathogen mediate- selection mechanisms (PMS). First, the heterozygote advantage hypothesis assumes that heterozygous indi- viduals are favored because they can recognize a broader range of pathogens [72, 73]. However, pub- lished evidence confirming a MHC-specific heterozy- gote advantage is limited [38, 74, 75]. Second, the rare allele advantage hypothesis assumes that uncom- mon alleles in the populations are likely to offer more protection than common alleles and thus confer a se- lective advantage [76]. In our data, we found rare al- leles in almost all the populations over the entire gradient. These rare alleles could be a potential source for defense against pathogens in these popula- tions. With our data we cannot distinguish among these two hypotheses and they are not mutually exclusive [35, 38, 39, 77, 78]. Further investigation regarding allele frequency distributions and parasite infection are needed to understand which mechanisms are responsible for main- taining genetic variation in relation to parasite resistance.

Selection/drift patterns along the gradient

R. arvalis populations showed clear structure and IBD in both adaptive and neutral markers despite being recently diverged in evolutionary time. Outlier analyses suggested that the MHC II exon 2 and RCO8640 loci have been af- fected by different evolutionary processes in the north- ern and southern cluster. We saw signs of diversifying selection only in the southern populations while all markers seem to be mainly influenced by drift processes in northern populations. However, this pattern could be enforced by the fact that we studied fewer populations in the northern cluster. A study in Scandinavian com- mon frog R. temporaria found that the impact of drift was higher in northern than in the southern populations probably because the northern populations were smaller and more isolated [79]. In our study, R. arvalis is at the northernmost range of its distribution and we suggest that populations are smaller and the connectivity among populations is poor in the northern cluster. Therefore, drift processes are likely to be more important at high latitudes due to a high degree of population fragmenta- tion and low effective population sizes (even though we cannot find a clear difference concerning effective sizes among southern and northern populations, Table 2).

The microsatellite locus RCO8640 was found to be under diversifying selection and had very low diversity in the northernmost populations (Luleå). This locus is lo- cated inside the up-regulated transcription factor gene C/EBP involved in the thyroid hormone pathway [62, 63], which is linked with adaptive divergence in larval developmental rate [56, 57]. We found that RCO8640 was under selection in the southern cluster, possibly sug- gesting selection on development rate along the south- ern part of the gradient, as found previously at local and broader geographical scales in northern European an- urans (e.g., [57, 80]). While we did not find significant diversifying selection in the northern cluster, F

ST

was still high along the northern part of the gradient. It would be very interesting to link variation in this locus to phenotypic variation in development rate along the latitudinal gradient.

When analyses were made on all the populations we

found evidence of diversifying selection on two (MHC II

exon 2 and RCO8640) and signs of stabilizing selection

on five loci. We find two reasons which might explain

why we find a high number of loci under stabilizing se-

lection: 1) microsatellites were developed using known

sequences of coding genes (See; [56, 57], 2) the long gra-

dient with a high global F

ST

allows for a better the detec-

tion of stabilizing selection. When the populations were

divided into a northern and southern cluster we found

evidence for selection in the southern cluster (three

cases of stabilizing and two cases of diversifying selec-

tion) while in the northern cluster we could only find

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signs of stabilizing selection on one locus. These pat- terns could reflect actual differences among regions in the relative importance of drift and selection but we ad- vise caution when interpreting the results of this study.

While drift is predicted to be more important in small and fragmented populations and selection is more im- portant in large connected ones [3], we cannot entirely rule out the possibility that the detected patterns may be partly due to lower sample sizes and fewer population contrasts in the north. So while in line with predictions from population genetic theory, these results should be deemed as tentative.

Combined effects of recolonization history, selection and drift

We suggest that genetic variation among the regions and populations can be explained by complex patterns of selection, drift and the two recolonization routes of Scandinavia since the last glaciation (see [81] for a simi- lar example). Our results provide an example of a situ- ation where the level of adaptive MHC II exon 2 diversity seems to be correlated with neutral diversity among populations. This is not always the case as deple- tion in overall genetic variation may or may not be cor- related with the amount of adaptive genetic variation [4]. Here we found that overall F

ST

and genetic diversity indexes (AR and H

E

) were substantially higher for the MHC locus compared to neutral microsatellite markers (even though correlated). These results suggest that the MHC II exon 2 locus is under diversifying selection and are in agreement with previous studies finding more dif- ferentiation in MHC than in neutral loci suggesting adaptation to local parasite faunas (e.g. [27, 78].

However, earlier studies on differentiation at MHC loci show substantial heterogeneity. Other studies have found no difference among population differentiation at MHC and neutral markers indicating the dominant role of drift, and yet others find no differentiation at the MHC indicating balancing selection maintaining genetic variation (see [82] for a recent example, summarized by [83]). There is a need for further studies of the processes shaping within and among population genetic variation in natural populations to further improve our under- standing on how genetic variation is geographically por- tioned and distributed.

Methods

Sample collection and DNA extraction

R. arvalis has a broad Euroasiatic distribution, from the North Sea coast to Siberia [67]. It lives in diverse habi- tats, from forests to pastures, and breeds in semi- permanent to permanent ponds and lakes. The centre of the distribution of Rana arvalis is located in the area of eastern Germany and western Poland (see distribution map in Sillero et al. [84]). We collected R. arvalis eggs in five regions from northern Germany (Hanover) to northern Sweden (Luleå) in 2014 and 2015 (Table 1, Additional file 10: Table S6; Figure 2). The eggs were collected at two sites in each region, with the exception of northern Germany and southern Sweden (Skåne), where samples were collected from three sites. The aver- age distance between collection sites in the same region was 20 km (range 8 to 50 km). The collection sites were ponds situated in flat terrain dominated by mixed forest, pastures and agricultural land. At each site we collected ca. ten eggs from each of 20 freshly laid clutches. The Table 2 Genetic variation at 9 neutral microsatellites in the studied populations

Locality Sampling area code n NA As PA H

o

H

e

AR Ne (LDNe) Ne (CNe)

Altwarmbüchen A 20 38 8 0.38 0.28 3.08 1.9 (1.0 –1.9) 0.7 (0.5 –1.0)

Mardorf Germany M 20 25 45 1 0.30 0.30 2.10 11.2 (2.3-inf) 68.9 (0.8 –346)

Seebeckwiesen Se 20 36 1 0.33 0.32 2.56 37.1 (8.5 - inf) 8.5 (0.2 –31.5)

Sjöhusen S 20 31 0 0.30 0.29 2.16 28.1 (4 - inf) inf (inf - inf)

Tvödora Skåne T 19 32 31 0 0.34 0.31 2.12 inf (10.1 - inf) inf (inf - inf)

Räften R 20 29 0 0.29 0.27 2.17 8 (2.1 –51.9) inf (inf - inf)

Österbybruk AÖ 20 53 16 0.51 0.43 3.95 1.0 (0.8 –1.2) 1.0 (0.7 –1.3)

Valsbrunna Uppsala V 20 33 46 0 0.32 0.32 2.13 inf (203.4 - inf) inf (inf - inf)

Holmsjön H 20 20 1 0.14 0.13 1.62 inf (19.2 - inf) inf (inf - inf)

Nydalasjön Umeå Ny 20 20 17 0 0.15 0.12 1.51 39 (19.6 -inf) 36.7 (0.0 –184)

Besbyn B 20 20 0 0.16 0.09 1.77 37 (2.7-inf) 4 (0.0 –20)

Ernäs Luleå E 20 17 20 1 0.12 0.08 1.45 inf (0.6-inf) 3.4 (0.0 –16.8)

Total 239 25 0.28 0.25 2.22

The populations are ordered from South to North. n = number of individuals; NA = alleles within a population; As = alleles within a sampling area; PA = private alleles; Ho= observed heterozygosity, He= expected heterozygosity; AR = allelic richness; Ne = effective population size by linkagedisequilibrium method (Ne (LDNe)) and by coancestry method (CNe)

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eggs were transported to Uppsala and kept in a climate room at 16 °C. After hatching the tadpoles (stage 25, [85]) were euthanized with an overdose of MS222, pre- served in 96% ethanol and stored at 4 °C until DNA extraction.

Genomic DNA was extracted from 240 tadpoles (one individual/clutch) using a high salt extraction precipita- tion protocol (modified from [86]). Purity and concen- tration of DNA were determined with a NanoDrop®

2000, spectrophotometer and Qubit®3.0 fluorometer Quantitation Kit (Invitrogen™). Species verification was carried out by mtDNA cytochrome b amplification followed by the addition of HaeIII restriction enzyme [87]. Digestions by HaeIII produces different, easily dis- tinguishable banding patterns in R. arvalis and R.

temporaria.

Microsatellite genotyping

We successfully genotyped all individuals at 18 microsat- ellite loci isolated from different Rana species, and tested another set of ten microsatellites without success (Additional file 1: Table S1). Some of the successfully ge- notyped loci are situated in or near coding regions, in- creasing the probability of these markers being under selection. PCR amplification was performed individually for each microsatellite. Reactions were performed in a final volume of 20 μl using an ABI 2720 thermal cycler.

The PCRs were done using either the Type-it® Microsat- ellite PCR Kit (Qiagen®, Sollentuna, Sweden) or Dream- Taq (Thermo Scientific) following the manufacturer’s instructions. Additional file 11: Table S7 specifies the used PCR type, the exact reaction mix and the thermo- cycling conditions for each PCR reaction. Prior to geno- typing, the PCR products were diluted in water (1:10, 1:30 or 1:50). 1 μl was mixed with 9.8 μl Hi-DiTM Formamide (P/N 4311320, Applied Biosystems) and 0.2 μl size standard (GeneScanTM, 600 LIZ®, Thermo Scientific) and run on 3730xl DNA Analyzer (Applied Biosystems). Samples were genotyped using GeneMap- per® Software 5 (Life TechnologiesTM).

MHC II exon 2 gene

Single locus amplification and preparation for sequencing We amplified the complete second exon (272 bp) of the single MHC II gene (corresponding to the β −2 domain) in R. arvalis. The primers ELF_1 (3′- GAGGTGATCCCTCCAGTCAGT-5′) and ELR_2 (3′- GCATAGCAGACGGAGGAGTC-5′) were designed based on primers sequences developed for R. pretiosa and R. luteiventris [88], amplifying a 338 bp fragment with the primers positioned in the flanking intron- exon region (Additional file 12: Figure S5). Both for- ward and reverse primers were modified for Illumina MiSeq sequencing with an individual 8 bp barcode

and a sequence of three N (to facilitate cluster identi- fication). Each amplicon was marked with an individ- ual combination of a forward and a reverse barcode for identification. PCR reactions were conducted in a total volume of 20 μl containing 1 μl of genomic DNA, 2 μl of 10X Dream taq buffer (Thermo scien- tific lab), 0.4 μl of 2 mM of each dNTP, 0.5 μl of each 10 μM primer (ELF_1 and ELR_2, respectively), 1.5 μl of Bovine serum albumine (BSA; 5 mg/ml) and 0.25 μl of Dream taq DNA polymerase (5 U/μl, Thermo scientific lab) in deionized water. Thermocy- cling was performed on the ABI 2720 (Applied Bio- systems®). An initial denaturation step of 3 min at 95 °C was followed by a touch-down procedure, con- sisting of 30 s at 95 °C, annealing for 30 s at temper- atures decreasing from 63 to 58 °C during the first 5 cycles (one detrimental degree per cycle) and end- ing with an extension step at 72 °C for 1 min. There- after, 30 similar cycles with a consistent annealing temperature of 58 °C followed, and PCR products were stored at 4 °C. All amplifications were carried out using filter tips in separate (pre- and post-PCR) rooms, and negative controls were included in all am- plifications to avoid contaminations. PCR products were run and visualized on a 1.5% agarose gel using gelgreen (BIOTIUM). To reduce the number of sam- ples for subsequent purification, 3 –9 PCR products with similar concentrations were pooled based on es- timations from the gel image. These sample pools were run on 1.5% agarose gel, the target band was ex- cised from the gel and extracted using the MinElute Gel Extraction Kit (Qiagen® Sollentuna, Sweden). The concentration of each sample pool was measured with Quant-iT PicoGreen dsDNA assay kit (Invitrogen Life Technologies, Stockholm, Sweden) on a fluorescence microplate reader (Ultra 384; Tecan Group Ltd., Männedorf, Switzerland). The final amplicon pooling was done according to the measured concentrations and consisted of equimolecular amounts of each sam- ple. A total of eight final amplicon pools were gener- ated, and libraries were prepared using the Illumina Truseq DNA PCR-Free Sample preparation kit (Illu- mina Inc., San Diego, CA). Four pools each were combined into a Miseq run, and sequencing of two Miseq 250 runs was carried out at the National Gen- omic Infrastructure (NGI), the SNP&SEQ Technology Platform hosted at SciLifeLab in Uppsala (Sweden).

Miseq data analyses

Sequencing data were extracted from the raw data and

combined into single forward reads using FLASH

(Magoč and Salzberg 2011), each of the eight amplicon

pools were analyzed independently. In total, eight fastq

files were generated and transformed into fasta

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(multifasta) files using Avalanche NextGen package (DNA Baser Sequence Assembler v4 (2013), Heracle BioSoft, www.DnaBaser.com). The jMHC software [89]

was used to remove primer sequences and unique tags, and to generate alignments of all variants per amplicon.

Generally, in MHC studies using NGS techniques rigor- ous quality control and filtering procedures have to be applied to distinguish PCR and sequencing artefacts from true MHC allele. In this study, however, the dis- tinction of true alleles and artefacts was greatly simpli- fied, for three reasons: 1) amplifying a single gene will only yield one or two true MHC alleles per amplicon, and the project was designed in a way that both 2) repli- cation rate and 3) per amplicon coverage was markedly high.

We assigned the two most frequent variants within each amplicon as valid MHC alleles that occurred in at least 3% of the reads [52, 90]. In a single locus system, it is not expected that chimera are generated in higher fre- quencies than the two parental alleles, and hence, no chimeric sequence that could have been taken for a valid MHC alleles could be detected when thoroughly check- ing the sequences per amplicon by eye.

If only one sequence exceeded the 3% rule, the ampli- con was scored as a homozygote. We discarded ampli- cons with <300 reads from the analysis for quality reasons. Among amplicons, valid MHC alleles had to be present in at least two amplicons (2-independent-PCR- criterion, [91]), therefore we replicated all amplicons that revealed unique MHC alleles in the second Miseq run, and ran every individual twice in the second Miseq run from independent PCRs. In addition, we used the DOC method, not assuming any specific number of loci to identify and estimate the number of alleles (Ai) per individual. This procedure is based on the break point in sequencing coverage between variants within each individ- ual and avoids choosing a subjective threshold to separate true alleles from artefacts. In this procedure, variants are sorted top-down by coverage, followed by the calculation of the coverage break point (DOC statistic) around each variant. The variant with the highest DOC value is as- sumed to be the last true allele (see [65]).

All valid allele sequences were imported and aligned in MEGA 6.0 [92]. Allele sequences were extensively compared to other sequences for the same locus: giant spiny frog (Quasipaa spinosa; GenBank: KM390904.1) natterjack toad (Epidalea calamita; GenBank:

HQ388291.1), mouse (Mus musculus; GenBank:

JN948541.1) and turkey (Meleagris gallopavo; GenBank:

DQ993255.2). According to the GT/AG rule [93] and in concordance with exon 2 in Odorrana tormota (JQ918829), the exon boundary was defined 16 bps downstream from intron-exon boundary determined in mouse. We removed the intron sequences for further

analyses and ended up with a MHC II exon 2 fragment of 272 bps. Valid MHC exon 2 alleles were named fol- lowing the nomenclature suggested by Klein [29]: a four digit abbreviation of the species name followed by gene*- numeration, e.g. Raar_DAB*01.

Data analyses Outlier analyses

We used the programs Lositan [94] and BayeScan v.

2.01 [95] to identify loci under divergent or uniform se- lection. The null distribution of F

ST

was simulated with 100,000 iterations implementing a stepwise mutation model (SMM) in Lositan. In Bayescan, outliers were de- tected by implementing the multinomial Dirichlet model. Outliers identified by at least one of the two methods were deemed as being non-neutral (Additional file 2: Table S2), and the term “neutral loci” is used to refer to the non-outlier loci included in subsequent analyses.

The microsatellite and MHC II exon 2 data set were divided in different subdatasets: the entire gradient with all the populations included in the analyses; a northern cluster (Umeå and Luleå); and a southern cluster (Germany, Skåne and Uppsala). We did the division after analyzing our data (see below) and in accordance with a bidirectional recolonization hypothesis of Scandinavia [67, 68]. For MHC II exon 2 we transformed the se- quence data into genotype data using PGBSpider [96] to be able to run the outlier test in the Lositan software while implementing a Stepwise Mutation Model (SMM) (see Fig. 1; Additional file 1: Table S1). We also ran the outlier analyses using an Infinite Alleles Model (IAM).

The results identifying outlier loci were independent of the mutation rate model, IAM or SMM (see Additional file 13: Figure S6). Outlier analyses were also repeated excluding the German populations from the data set (See Additional file 14: Table S8 and Additional file 15:

Figure S7).

Analyses of microsatellite data

Input files were converted for different analyses program formats using the Excel add-in Microsatellite toolkit [97]. The frequency of null alleles was estimated with two different softwares: FreeNA [98] and Genepop [99].

To assess neutral genetic diversity, expected heterozy-

gosity (H

E

), observed heterozygosity (H

O

) and allelic

richness (AR), allele numbers rarified to the smallest

sample size were calculated for each locality in FSTAT

2.9.3.2 [100] Multi-locus means were obtained using

Microsatellite toolkit. Effective population size was esti-

mated based on two different methods: the linkage dis-

equilibrium method and the coancestry method using

the software Ne estimator [101].

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To examine population structure and differentiation, global and pair-wise F

ST

between all the populations [102] were calculated according to Nei et al. [103], as implemented in the R-package hierfstat [104] We used the ENA correction described in [98] and F’

ST

/G’

ST

cor- rections [105] for all the F

ST

values when appropriate.

We tested for deviation from Hardy-Weinberg equilib- rium (HWE) in ARLEQUIN v. 3.5. [106] and tested for Isolation by distance (IBD) by calculating pair-wise F

ST

and log distance (km) for every population pair. Pair- wise F

ST

was transformed based on Slatkin’s (1995) data adjustment (F

ST

/(1-F

ST

)). The Euclidean distance matrix was estimated using the R package Geosphere (see Hijmans et al. [107]). To test if pair-wise F

ST

was spatially auto-correlated, Mantel tests were performed in R running the package Adegenet [108].

We visualized the spatial structure of microsatellite data using discriminant analysis of principal components (DAPC) implemented in Adegenet [108]. The optimal number of clusters for the DAPC was chosen based on the lowest Bayesian Information Criterion (BIC) value for the different clustering solutions, which coincided with a sharp break in the curve of BIC values as a func- tion of k. Genetic clustering was also analyzed using STRUCTURE [109] to find the most likely number of genetic clusters (K) and assign individuals to these clus- ters assuming an admixture model and correlated allele frequencies with 10,000 burn-in steps and 100,000 MCMC (Markov chain Monte Carlo) steps. We investi- gated the likelihood of various numbers of K (2–5), fol- lowing the approach suggested by Evanno et al. [110].

To infer the most likely K we visualized and compared the different likelihood distribution plots and ΔK plots produced by STRUCTURE HARVESTER [111] Runs for all the K were averaged using the LargeGreedy algorithm in CLUMPP software [112], which aligns cluster assign- ment across replicate analyses and were then visualized with DISTRUCT [113]. We estimated and visualized the gene flow patterns between all pairs of population sam- ples by using divMigrate-online [66], which can detect asymmetric gene flow patterns. We included 12 popula- tions, used G

ST

(as implemented in divMigrate-online) as the differentiation metric and set the filter threshold to 0.20; 0.30; 0.40, respectively. Confidence limits on gene flow estimates were determined by 1000 bootstrap replicates.

Analyses of MHC II exon 2

To assess genetic diversity in the MHC II exon 2 stand- ard diversity indices (H

E

, H

O

, and allelic frequencies) were calculated for each locality in Arlequin v 3.5 [106]

Allelic richness was calculated in FSTAT 2.9.3.2 [100].

Allele frequency plots were created in R using the

“ggplot2” package [114].

Global population differentiation, HWE expected values and a pair-wise distance matrix for all the populations were computed in ARLEQUIN v. 3.5; we ran 1000 permutations to allow calculation of confi- dence limits. We tested for Isolation by distance (IBD; [115]), as described above for the microsatellite data.

Comparing differentially selected and neutral loci

To compare population differentiation among the differ- entially selected loci, as revealed by the outlier analyses (above), we plotted the standardized F’

ST

for MHC exon 2 and RCO8640, respectively, against the differentiation for neutral microsatellites, G’

ST

[105]. We then com- puted the restricted major axis regression slopes for the northern and southern population comparisons separ- ately for each comparison with a differentially selected marker. We hypothesized that if population differenti- ation was stronger among southern populations, the slope of this regression would be steeper than among northern populations.

Conclusion

In summary, populations were shown to be subjected to different selective regimes and combined with different historical demographic patterns affecting the strength of genetic drift, a complex pattern of differentiation have evolved along the gradient. Some loci are more divergent than expected by drift among populations due to diversi- fying selection while others are more uniform among populations due to stabilizing selection. Our data show a high number of MHC exon 2 alleles in comparison to other European amphibian species. Both overall and MHC genetic variation are lower at northern latitudes which suggest a high risk of extinction when confronted with emerging pathogens and climate change. These re- sults emphasize the importance of latitudinal gradient studies in order to elucidate and understand the evolu- tionary processes shaping genetic variation among nat- ural populations.

Additional files

Additional file 1: Table S1. Microsatellites used in the study. (PDF 40 kb) Additional file 2: Table S2. 16 microsatellites outlier analyses results from Lositan and Bayescan. (PDF 31 kb)

Additional file 3: Table S3. 15 microsatellites and MHC II exon 2 outlier analyses results from Lositan and Bayescan. Analyses were performed independently considering: all the gradient, northern populations as a cluster and southern populations as another cluster. (PDF 31 kb) Additional file 4: Figure S1. Isolation-by-distance a) for MHC class II and b) for microsatellite (MS) shown as Slatkin ’s linearized pair-wise F

ST

“(F

ST

/(1-F

ST

)) ” as a function of the natural logarithm of distance (km) be-

tween locality pairs. MHC class II is represented by blue circles and MS

are represented by black stars. (PDF 2099 kb)

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Additional file 5: Figure S2. Results of STRUCTURE analyses using the Admixture model. Average individual assignment probability (y-axis) of individuals for four values of K. Sampled populations are given below the plot and country, region or province of origin is given above. Delta K and Mean estimation Ln probability of the data are shown for different values of K. (PDF 326 kb)

Additional file 6: Figure S3. Discriminant component analyses (DAPC) of 9 neutral microsatellites for all the individuals. All individuals from the north cluster together to the right of the figure. (PDF 1539 kb) Additional file 7: Table S4. Pairwise FSTs. a) MHC gene FST pairwise comparisons and b) neutral microsatellite FST pairwise comparisons, respectively. (PDF 49 kb)

Additional file 8: Figure S4. Colour scheme for the allele distribution maps a) MHC gene and b) RCO8640 gene. (PDF 875 kb)

Additional file 9: Table S5. Relative allele frequency for all the MHC alleles found along the latitudinal gradient. N = number of individual per population, AS = Alleles within sampling areas. (PDF 101 kb)

Additional file 10: Table S6. Information about pond locations along the latitudinal gradient. (PDF 48 kb)

Additional file 11: Table S7. PCR conditions for a total volume of 10 μl reaction for the neutral microsatellites finally used in the study.

(PDF 69 kb)

Additional file 12: Figure S5. a) Primers were modified for Illumina Miseq consisting of a 8 bps barcode assignment and a sequence of three N Barcodes represented in pink (forward direction; “5- > 3”) and in orange (reverse direction; 3- > 5 ″). The NNN sequence is shown in blue.

Possible primer pair combinations are presented in two different boxes (brown and orange, respectively). Every forward primer was combined with 9 different reverse primers for every pool. A total of four pools were constructed in the study: blue (A), purple (B), orange (C) and red (D). b) Outline of the library preparation. (PDF 173 kb)

Additional file 13: Figure S6. Results of outlier analyses by Lositan software in MHC class II exon 2 and RCO8640 according to a) Infinite allele model approach (IAM) b) Stepwise mutation model approach (SMM). (PDF 140 kb)

Additional file 14: Table S8. 15 microsatellites and MHC II exon 2 outlier analyses results from Lositan and Bayescan. Analyses were performed independently considering: all the gradient and southern population, excluding all the German locations. Overall FST for the gradient excluding all population from Germany was 0.35. (PDF 17 kb) Additional file 15: Figure S7. Results of outlier analyses by Lositan software for 15 microsatellites neutral markers, MHC class II exon 2 and RCO8640 according to the Stepwise mutation model approach (SMM) and excluding all the German populations. (PDF 856 kb)

Acknowledgments

We thank Alex Richter-Boix and Soraya Rouifed for invaluable help in the field. Many thanks to Patrik Rödin Mörch and Peter Halvarsson for help on the statistical analyses, to Emilio Virgós Cantalapiedra for the helpful comments on the manuscript and to Kevin Mulder for the help with the primer design.

We are thankful to Anders Hallengren (Länstyrelsen Skåne), Anders Forsgren and Stefan Andersson (Piteå Kommun), Thomas Brandt (Ökologische Schutzstation Steinhuder Meer e.V), and Bernd Rittberg and Andreas Jacob (Authority for Nature Protection of Lower Saxony and the Hannover region) for helping with the sampling locations and permissions. Funding was provided by The Swedish Research Council Formas (grant 146400178 to JH), Stiftelsen Oscar och Lili Lamms Minne (grant DO2013-0013 to JH), Stiftelsen för Zoologisk Forskning and Swedish Research Council (grant 621-2013-4503 to AL).

Funding

Funding was provided by The Swedish Research Council Formas (grant 146,400,178 to JH), Stiftelsen Oscar och Lili Lamms Minne (grant DO2013 – 0013 to JH), Stiftelsen för Zoologisk Forskning and Swedish Research Council (grant 621 –2013-4503 to AL) for the study design, data collection and analyses.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors ’ contributions

MC collected the data, did the practical work in the laboratory, analyzed the data and wrote the manuscript. EZL did some of the practical work in the laboratory and analyzed some data as a master project. EL participated in data collection. YML, AL and JH made substantial contributions to conception, design, analysis and interpretation of data. All the authors read and approved the final version of the manuscript.

Ethics approval and consent to participate Not applicable.

Consent for publication Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher ’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details

1

Animal Ecology/Department of Ecology and Genetics, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden.

2

Present address: Department of Neurogenetics of Vocal Communication, Max Planck Institute of

Psycholinguistics, Box 310, 6500 Nijmegen, Netherlands.

3

Present address:

Université Claude Bernard - Lyon I, CNRS, UMR 5023 – LEHNA, 3–6, rue Raphaël Dubois - Bâtiments Darwin C and Forel, 69622 Villeurbanne Cedex 43, Boulevard du 11 novembre, 1918 Lyon, France.

Received: 17 April 2017 Accepted: 26 July 2017

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References

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